A REAL-TIME IOT-BASED INTELLIGENT VEHICLE MONITORING SYSTEMFOR SUSTAINABLE TRANSPORTATION

Abstract

Vehicular safety, operational efficiency, and predictive maintenance are major challenges in modern transportation systems, particularly in high-density urban environments and long-distance fleet operations. Traditional vehicle monitoring methods rely on periodic manual inspections and reactive diagnostics, which often result in delayed fault detection, higher maintenance costs, and safety risks. To overcome these limitations, this study proposes an Internet of Things (IoT)-based Smart Vehicle Monitoring System that enables continuous, real-time vehicular telemetry and intelligent diagnostics. To address these limitations, this study proposes an Internet of Things (IoT)-based Smart Vehicle Monitoring System that enables continuous, real-time monitoring of vehicle performance. The proposed architecture employs an ESP32 microcontroller integrated with multiple sensors to measure engine temperature, fuel level, vibration, GPS-based location, accelerometer-based motion, and environmental parameters such as ambient temperature and humidity. This intelligent monitoring approach enhances transportation safety and operational efficiency while supporting smart mobility and resilient infrastructure, contributing to Sustainable Development Goal (SDG) 9: Industry, Innovation and Infrastructure and SDG 11: Sustainable Cities and Communities.

To improve vehicle condition assessment, the system introduces a Vehicle Health Index (VHI) model developed using min–max normalization and a weighted multi-sensor data fusion technique. Unlike traditional threshold-based alert mechanisms, the proposed framework evaluates the combined influence of mechanical stress indicators and environmental conditions to generate a comprehensive health score. This score reflects real-time vehicle stability, maintenance priority, and operational risk..